Machine Learning of Coarse-Grained Molecular Dynamics Force Fields.

ABSTRACT: Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free ener

SUBMITTER: Wang J 

PROVIDER: S-EPMC6535777 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature